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There's a lot to critique but this is a really weird one (page 49 if anyone is following). The whole thing is 5 sentences and all the space is because a diagram and code block. The 5 sentences should be the thing to complain about
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
11–20 of 177 posts
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#12First time I've seen one of these books where I wished there was more words and less math. Usually it is quite the opposite. But this book seems written as if they wanted to avoid natural language at all costs.
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#13Is anyone using any of this math? My guess is no. At best it provides "moral support" for deep learning researchers who want to feel reassured that what they are attempting to do is not impossible. Glad to be proven wrong, though.
There's something I tell my students. You don't need math to make good models, but you do need to know math to know why your models are wrong.
So yes, math is needed. If you don't have math you're going to hoodwink yourself into thinking you can get to AGI by scale alone. You'll just use transformers everywhere because that's what everyone else does and you'll get confused between activation functions. You'll make models and models that work, but there's a big difference in working models and knowing where to expect your models to fail and understanding their limitations.
I feel a lot of people just look at test set results and expect that to mean that the model isn't overfitting. (not to mention tuning hps based on test set results)
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#14[dead]
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#15Is anyone using any of this math? My guess is no. At best it provides "moral support" for deep learning researchers who want to feel reassured that what they are attempting to do is not impossible. Glad to be proven wrong, though.
In the latter part of the book that covers PINNs and other PDE methods, it helps to frame these using the same kind of functional analysis that is used to develop more traditional numerical methods. In this case, it provides a way for practitioners to verify the physical consistency between the various methods.
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#16This is in Tensorflow.
Would rather see a numpy version or something along those lines so that students can better understand what each step looks like in code.
I concur on the comments noting lack of explanation for the notation/lemmas/proof.
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#17Is anyone using any of this math? My guess is no. At best it provides "moral support" for deep learning researchers who want to feel reassured that what they are attempting to do is not impossible. Glad to be proven wrong, though.
There's something I tell my students. You don't need math to make good models, but you do need to know math to know why your models are wrong. So yes, math is needed. If you don't have math you're going to hoodwink yourself into thinking you can get to AGI by scale alone. You'll just use transformers everywhere because that's what everyone else does and you'll get confused between activation functions. You'll make mo…
Oh sure. I say the same to my students.
But the particular spin on this book makes it look to non-experts that this is the math you need to do something useful with deep learning. And that's just not true.
Certainly you need to understand what you're optimizing, how your optimizer works, what your objective function is doing, etc. But the vast majority of people don't need to know about theoretical approximation results for problems that they will never actually encounter in real life, etc. For example, I have never used used anything like "6.1.3 Lyapunov-type stability for GD optimization" in a decade of ML research. I'm sure people do! But not on the kinds of problems I work on.
Just look at the comments here. People are complaining about the lack of context, but this is fine for the audience the book is aimed at. It's just the average HN reader.
I think it would be better if the authors chose a different title. As it stands, non-experts will be attracted and then be put off, and experts will think the book is likely to be too generic.
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#18This is in Tensorflow. Would rather see a numpy version or something along those lines so that students can better understand what each step looks like in code. I concur on the comments noting lack of explanation for the notation/lemmas/proof.
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Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#19First time I've seen one of these books where I wished there was more words and less math. Usually it is quite the opposite. But this book seems written as if they wanted to avoid natural language at all costs.
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Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#20[flagged]
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